Our goal is to improve the scalability of robotic data collection by providing assistance to the human operator during teleoperation. We take inspiration from other fields of machine learning, such as semantic segmentation, where costly labeling processes have been substantially accelerated by providing human annotators with learned assistance systems, e.g., in the form of rough segmentation estimates, that drastically reduce the labeling burden [1]}, [2]}. Similarly, we propose to train assistive policies, that can automate control of repeatedly demonstrated behaviors and ask for user input only when facing a novel situation or when unsure which behavior to execute. Thereby, we aim to reduce the mental load of the human operator and enable scalable teleoperation by allowing a single operator to perform data collection with multiple robots in parallel.
